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Models/BiSeNet V2-Large(Cityscapes-Pretrained)

BiSeNet V2-Large(Cityscapes-Pretrained)

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical3 results

Semantic SegmentationonCamVid
mIoU· uses extra data· 2020-04-05
78.5
best: 82 (PIDNet-M (Cityscapes-Pretrained))
SOTA
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147
Semantic SegmentationonCamVid
Frame (fps)· uses extra data· 2020-04-05
32.7
best: 222.3 (PP-LiteSeg-T)
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147
Semantic SegmentationonCamVid
Time (ms)· uses extra data· 2020-04-05
30.6
best: 227 (Dilation10)
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147

Audio3 results

10-shot image generationonCamVid
mIoU· uses extra data· 2020-04-05
78.5
best: 82 (PIDNet-M (Cityscapes-Pretrained))
SOTA
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147
10-shot image generationonCamVid
Frame (fps)· uses extra data· 2020-04-05
32.7
best: 222.3 (PP-LiteSeg-T)
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147
10-shot image generationonCamVid
Time (ms)· uses extra data· 2020-04-05
30.6
best: 227 (Dilation10)
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation arXiv:2004.02147